Oncology/Hematology

Unsupervised Tissue Concepts for Explainable Sarcoma Subtype Prediction from H&E

Soft tissue sarcomas are a rare, heterogeneous group of tumors whose diagnosis remains challenging because of overlapping morphology and limited access to sarcoma-specialized pathologists. Although pathology foundation models have shown promise in computational pathology, their clinical translation remains limited by insufficient interpretability, particularly in diagnostically complex settings su...

Clonal haematopoiesis without identified genetic drivers: insights from analyses of 407,512 individuals

Clonal haematopoiesis (CH) becomes ubiquitous as humans age. The role of somatic driver mutations in its development has been studied widely, but little is known about CH without identified genetic drivers, also known as "CH with unknown drivers" (CH-UD). A fundamental unresolved question is whether CH-UD is driven by undiscovered somatic genetic drivers or by other cell-heritable traits. Here, to...

Domain-adversarial learning predicts clinically actionable drug combination synergy in leukemia patients using bulk transcriptomics data

Deep learning has gained popularity in drug combination synergy prediction; however, DL models require large training datasets from cell line pharmaco...

Mapping Tumor-Microenvironment dependencies with TMEformer: A spatial foundation framework enabling in silico perturbation

Despite the fundamental role of spatial context in driving tumor progression, most current computational models for virtual perturbation have largely ...

Real-World Validation of Machine Learning Models for HIV Treatment Adherence Prediction and Care Gap Quantification: A Multi-Country Analysis of 192,732 Clinical Records

Delayed diagnosis and poor antiretroviral therapy (ART) adherence remain primary drivers of HIV-related morbidity in low-resource settings, yet real-w...

Predicting Distant Melanoma Metastasis at Diagnosis Using Machine Learning

Distant melanoma metastasis at the time of diagnosis is uncommon, but has major implications for patient prognosis and treatment selection. However, f...

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two lar...

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothes...

Engineering Endogenous T Cell Receptors to Recognize Cancer Neoantigens Using a Hybrid Physics-AI Approach

T cell receptors (TCRs) are critical for immune surveillance and successful adaptive immune response against foreign antigens. TCRs drive this key arm...

Transcript architecture predetermines m6A remodeling and sensory neuron vulnerability in chemotherapy-induced peripheral neuropathy

Whether individual transcripts carry intrinsic features that predetermine their response to external perturbations is unknown. Here we used nanopore d...

A Multimodal Neural Network Model for Early Recurrence Prediction in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is the most common primary lung cancer worldwide. Despite advancements in...

Interpretable Predictive Modeling for Medical Data Using Boolean Rule-aware Regression

Purpose: In clinical practice, accurate prediction of disease risk must be accompanied by transparent, human-understandable explanations to support di...

Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification

In histopathology, human experts primarily rely on color as a means of enhancing contrast to interpret tissue morphology, whereas machine vision model...

Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability

Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability...

Overweight status drives early tumor microenvironment reprogramming in pancreatic ductal adenocarcinoma: a cell-type-resolved Bayesian hierarchical modeling and interactome analysis

Background: Obesity significantly increases the risk of prognosis and clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). While research on ...

Learning from Drops: AI-Guided Integration of Liquid Biopsy Features in Cancer Studies

Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are cruci...

A unified benchmark of synthetic data generation for clinical transcriptomic cancer cohorts

Achieving a trade-off between biological utility and patient privacy remains a key challenge for secure data sharing when applying transcriptomic clin...

Deep Learning for Automated Meningioma Segmentation: Toward Clinical Integration and Workflow Efficiency

Background: Meningiomas are the most common primary intracranial tumors in adults, and volumetric assessment increasingly guides surveillance and trea...

Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is an aggressive asbestos-linked cancer with limited therapeutic options and a dismal 5-year survival rate of ~5%...

Deep Learning for Cross-Domain Spatial Transcriptomic Modeling of Tissue Repair

Spatial transcriptomics enables investigation of tissue organization while preserving molecular and spatial information within intact tissues. However...